h-INcLOF: A DYNAMIC IOCAI OUTIIER DETECTION AIGORITHM FOR DATA STREAMS

摘要：

With the development of the data stream technology, The way to detect anomalies in data streams accurately has been widespreadly concerned.According to the problem that the distribution of the number of the outliers in data streams is unstable, in this paper, the n-lncLOF incremental outlier detection algorithm is proposed which could adjust the n threshold automaticly. The experiment of oultlier detection of the data stream proves that n-lncLOF algorithm could adjust to the change of the number of outliers effectively and it not only improves the detection rate greatly but also lowers the false alarm rate compared to the original incremental algorithm.

关键词： Outlier n-threshold Data Streams Data Mining

作者: Ke GAO Feng-Jing SHAO Ren-Cheng SUN

作者单位: College of Information Engineering Qingdao University Qingdao 266071, China

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Signal Processing System(2010年信号处理系统国际会议 ICSPS 2010)

会议地点: 大连

会议语种:英文

页码: 1019-1023

在线出版日期: 2010-07-05（万方平台首次上网日期，不代表论文的发表时间）